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+import os
+import requests
+import streamlit as st
+from io import BytesIO
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from transformers import pipeline, AutoModel, AutoTokenizer
+import torch
+
+# Set up the page configuration as the first Streamlit command
+st.set_page_config(page_title="RAG-based PDF Chat", layout="centered", page_icon="📄")
+
+# Load the summarization pipeline model
+@st.cache_resource
+def load_summarization_pipeline():
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")  # Use a summarization model
+    return summarizer
+
+summarizer = load_summarization_pipeline()
+
+# List of Hugging Face PDF URLs
+PDF_URLS = [
+    "https://huggingface.co/spaces/tahirsher/GenAI_Lawyers_Guide/blob/main/administrator92ada0936848e501425591b4ad0cd417.pdf",
+    "https://huggingface.co/spaces/tahirsher/GenAI_Lawyers_Guide/blob/main/Pakistan%20Penal%20Code.pdf",
+]
+
+# Helper function to convert Hugging Face blob URLs to direct download URLs
+def get_huggingface_raw_url(url):
+    if "huggingface.co" in url and "/blob/" in url:
+        return url.replace("/blob/", "/resolve/")
+    return url
+
+# Fetch and extract text from PDF files hosted on Hugging Face
+def fetch_pdf_text_from_huggingface(urls):
+    text = ""
+    for url in urls:
+        raw_url = get_huggingface_raw_url(url)
+        response = requests.get(raw_url)
+        if response.status_code == 200:
+            pdf_file = BytesIO(response.content)
+            try:
+                pdf_reader = PdfReader(pdf_file)
+                for page in pdf_reader.pages:
+                    page_text = page.extract_text()
+                    if page_text:
+                        text += page_text
+            except Exception as e:
+                st.error(f"Failed to read PDF from URL {url}: {e}")
+        else:
+            st.error(f"Failed to fetch PDF from URL: {url}")
+    return text
+
+# Split text into manageable chunks
+@st.cache_data
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# Initialize embedding function
+embedding_function = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Create a FAISS vector store with embeddings
+@st.cache_resource
+def load_or_create_vector_store(text_chunks):
+    vector_store = FAISS.from_texts(text_chunks, embedding=embedding_function)
+    return vector_store
+
+# Generate summary based on the retrieved text
+def generate_summary_with_huggingface(query, retrieved_text):
+    # Concatenate query and retrieved text for summarization
+    summarization_input = f"{query}\n\nRelated information:\n{retrieved_text}"
+    
+    # Truncate input to fit within the model’s token length limit (approximately 1024 tokens)
+    max_input_length = 1024
+    summarization_input = summarization_input[:max_input_length]
+
+    # Generate the summary
+    summary = summarizer(summarization_input, max_length=500, min_length=50, do_sample=False)
+    return summary[0]["summary_text"]
+
+# Generate response for user query
+def user_input(user_question, vector_store):
+    docs = vector_store.similarity_search(user_question)
+    context_text = " ".join([doc.page_content for doc in docs])
+    return generate_summary_with_huggingface(user_question, context_text)
+
+# Main function to run the Streamlit app
+def main():
+    st.title("📄 Gen AI Lawyers Guide")
+
+    # Load documents from Hugging Face
+    raw_text = fetch_pdf_text_from_huggingface(PDF_URLS)
+    text_chunks = get_text_chunks(raw_text)
+    vector_store = load_or_create_vector_store(text_chunks)
+
+    # User question input
+    user_question = st.text_input("Ask a Question:", placeholder="Type your question here...")
+
+    if st.button("Get Response"):
+        if not user_question:
+            st.warning("Please enter a question before submitting.")
+        else:
+            with st.spinner("Generating response..."):
+                answer = user_input(user_question, vector_store)
+                st.markdown(f"**🤖 AI:** {answer}")
+
+if __name__ == "__main__":
+    main()